Our article “New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings” is finally appearing in print in the Journal of Planning Education and Research‘s forthcoming winter issue. We collected, validated, and analyzed 11 million Craigslist rental listings to discover fine-grained patterns across metropolitan housing markets in the United States.
Continue reading New Article: Craigslist Housing Markets in JPER
Tools like WalkScore visualize how “walkable” a neighborhood is in terms of access to different amenities like parks, schools, or restaurants. It’s easy to create accessibility visualizations like these ad hoc with Python and its pandana library. Pandana (pandas for network analysis – developed by Fletcher Foti during his dissertation research here at UC Berkeley) performs fast accessibility queries over a network. I’ll demonstrate how to use it to visualize urban walkability. My code is in these IPython notebooks in this urban data science course GitHub repo.
First I give pandana a bounding box around Berkeley/Oakland in the East Bay of the San Francisco Bay Area. Then I load the street network and amenities from OpenStreetMap. In this example I’ll look at accessibility to restaurants, bars, and schools. But, you can create any basket of amenities that you are interested in – basically visualizing a personalized “AnythingScore” instead of a generic WalkScore for everyone. Finally I calculate and plot the distance from each node in the network to the nearest amenity:
Continue reading How to Visualize Urban Accessibility and Walkability
I am presenting at the 2015 Conference on Complex Systems tomorrow in Tempe, Arizona. My paper is on methods for assessing the complexity of urban design. If you’re attending the conference, come on by!
Here’s the paper.
Here’s the abstract:
Continue reading Urban Design and Complexity
The fall semester begins next week at UC Berkeley. For the third year in a row, Paul Waddell and I will be teaching CP255: Urban Informatics and Visualization, and this is my first year as co-lead instructor.
This masters-level course trains students to analyze urban data, develop indicators, conduct spatial analyses, create data visualizations, and build interactive web maps. To do this, we use the Python programming language, open source analysis and visualization tools, and public data.
This course is designed to provide future city planners with a toolkit of technical skills for quantitative problem solving. We don’t require any prior programming experience – we teach this from the ground up – but we do expect prior knowledge of basic statistics and GIS.
Update, September 2017: I am no longer a Berkeley GSI, but Paul’s class is ongoing. Check out his fantastic teaching materials in his GitHub repo. From my experiences here, I have developed a cycle of course materials, IPython notebooks, and tutorials towards an urban data science course based on Python, available in this GitHub repo.
Continue reading Urban Informatics and Visualization at UC Berkeley